Overview

Dataset statistics

Number of variables30
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory344.7 KiB
Average record size in memory240.1 B

Variable types

Numeric16
Categorical14

Alerts

Age is highly overall correlated with Total_working_yearsHigh correlation
Education_Field is highly overall correlated with DepartmentHigh correlation
Job_Role is highly overall correlated with DepartmentHigh correlation
Monthly_Income is highly overall correlated with Total_working_yearsHigh correlation
Salary_Hike_Percentage is highly overall correlated with Performance_RatingHigh correlation
Total_working_years is highly overall correlated with Age and 2 other fieldsHigh correlation
Years_company is highly overall correlated with Total_working_years and 3 other fieldsHigh correlation
Years_current_role is highly overall correlated with Years_company and 2 other fieldsHigh correlation
Years_Promotion is highly overall correlated with Years_company and 1 other fieldsHigh correlation
Years_curr_mangaer is highly overall correlated with Years_company and 1 other fieldsHigh correlation
Department is highly overall correlated with Education_Field and 1 other fieldsHigh correlation
Marital_Status is highly overall correlated with Stack_Option_LevelHigh correlation
Performance_Rating is highly overall correlated with Salary_Hike_PercentageHigh correlation
Stack_Option_Level is highly overall correlated with Marital_StatusHigh correlation
Education_Field has 27 (1.8%) zerosZeros
Job_Role has 131 (8.9%) zerosZeros
Num_comp_worked has 197 (13.4%) zerosZeros
Train_time_last_year has 54 (3.7%) zerosZeros
Years_company has 44 (3.0%) zerosZeros
Years_current_role has 244 (16.6%) zerosZeros
Years_Promotion has 581 (39.5%) zerosZeros
Years_curr_mangaer has 263 (17.9%) zerosZeros

Reproduction

Analysis started2023-05-12 09:54:41.722612
Analysis finished2023-05-12 09:54:55.760412
Duration14.04 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92381
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:55.796600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1353735
Coefficient of variation (CV)0.24741146
Kurtosis-0.40414514
Mean36.92381
Median Absolute Deviation (MAD)6
Skewness0.4132863
Sum54278
Variance83.455049
MonotonicityNot monotonic
2023-05-12T15:24:55.848064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35 78
 
5.3%
34 77
 
5.2%
36 69
 
4.7%
31 69
 
4.7%
29 68
 
4.6%
32 61
 
4.1%
30 60
 
4.1%
33 58
 
3.9%
38 58
 
3.9%
40 57
 
3.9%
Other values (33) 815
55.4%
ValueCountFrequency (%)
18 8
 
0.5%
19 9
 
0.6%
20 11
 
0.7%
21 13
 
0.9%
22 16
 
1.1%
23 14
 
1.0%
24 26
1.8%
25 26
1.8%
26 39
2.7%
27 48
3.3%
ValueCountFrequency (%)
60 5
 
0.3%
59 10
0.7%
58 14
1.0%
57 4
 
0.3%
56 14
1.0%
55 22
1.5%
54 18
1.2%
53 19
1.3%
52 18
1.2%
51 19
1.3%

Attrition
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1233 
1
237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1233
83.9%
1 237
 
16.1%

Length

2023-05-12T15:24:55.894099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:55.940806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1233
83.9%
1 237
 
16.1%

Most occurring characters

ValueCountFrequency (%)
0 1233
83.9%
1 237
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1233
83.9%
1 237
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1233
83.9%
1 237
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1233
83.9%
1 237
 
16.1%

Business_Travel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2
1043 
1
277 
0
150 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 1043
71.0%
1 277
 
18.8%
0 150
 
10.2%

Length

2023-05-12T15:24:55.977518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:56.019569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2 1043
71.0%
1 277
 
18.8%
0 150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
2 1043
71.0%
1 277
 
18.8%
0 150
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1043
71.0%
1 277
 
18.8%
0 150
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1043
71.0%
1 277
 
18.8%
0 150
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1043
71.0%
1 277
 
18.8%
0 150
 
10.2%

DailyRate
Real number (ℝ)

Distinct886
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.48571
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:56.061968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile165.35
Q1465
median802
Q31157
95-th percentile1424.1
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.5091
Coefficient of variation (CV)0.50282403
Kurtosis-1.2038228
Mean802.48571
Median Absolute Deviation (MAD)344
Skewness-0.0035185684
Sum1179654
Variance162819.59
MonotonicityNot monotonic
2023-05-12T15:24:56.109228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 6
 
0.4%
408 5
 
0.3%
530 5
 
0.3%
1329 5
 
0.3%
1082 5
 
0.3%
329 5
 
0.3%
829 4
 
0.3%
1469 4
 
0.3%
267 4
 
0.3%
217 4
 
0.3%
Other values (876) 1423
96.8%
ValueCountFrequency (%)
102 1
 
0.1%
103 1
 
0.1%
104 1
 
0.1%
105 1
 
0.1%
106 1
 
0.1%
107 1
 
0.1%
109 1
 
0.1%
111 3
0.2%
115 1
 
0.1%
116 2
0.1%
ValueCountFrequency (%)
1499 1
 
0.1%
1498 1
 
0.1%
1496 2
0.1%
1495 3
0.2%
1492 1
 
0.1%
1490 4
0.3%
1488 1
 
0.1%
1485 3
0.2%
1482 1
 
0.1%
1480 2
0.1%

Department
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
961 
2
446 
0
 
63

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 961
65.4%
2 446
30.3%
0 63
 
4.3%

Length

2023-05-12T15:24:56.155253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:56.198475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 961
65.4%
2 446
30.3%
0 63
 
4.3%

Most occurring characters

ValueCountFrequency (%)
1 961
65.4%
2 446
30.3%
0 63
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 961
65.4%
2 446
30.3%
0 63
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 961
65.4%
2 446
30.3%
0 63
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 961
65.4%
2 446
30.3%
0 63
 
4.3%

Distance_From_Home
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:56.235904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1068644
Coefficient of variation (CV)0.88189823
Kurtosis-0.2248334
Mean9.192517
Median Absolute Deviation (MAD)5
Skewness0.958118
Sum13513
Variance65.721251
MonotonicityNot monotonic
2023-05-12T15:24:56.277333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 211
14.4%
1 208
14.1%
10 86
 
5.9%
9 85
 
5.8%
3 84
 
5.7%
7 84
 
5.7%
8 80
 
5.4%
5 65
 
4.4%
4 64
 
4.4%
6 59
 
4.0%
Other values (19) 444
30.2%
ValueCountFrequency (%)
1 208
14.1%
2 211
14.4%
3 84
 
5.7%
4 64
 
4.4%
5 65
 
4.4%
6 59
 
4.0%
7 84
 
5.7%
8 80
 
5.4%
9 85
5.8%
10 86
5.9%
ValueCountFrequency (%)
29 27
1.8%
28 23
1.6%
27 12
0.8%
26 25
1.7%
25 25
1.7%
24 28
1.9%
23 27
1.8%
22 19
1.3%
21 18
1.2%
20 25
1.7%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Length

2023-05-12T15:24:56.318966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:56.364919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Education_Field
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.247619
Minimum0
Maximum5
Zeros27
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:56.402382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3313691
Coefficient of variation (CV)0.59234642
Kurtosis-0.68808083
Mean2.247619
Median Absolute Deviation (MAD)1
Skewness0.55037125
Sum3304
Variance1.7725437
MonotonicityNot monotonic
2023-05-12T15:24:56.437520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 606
41.2%
3 464
31.6%
2 159
 
10.8%
5 132
 
9.0%
4 82
 
5.6%
0 27
 
1.8%
ValueCountFrequency (%)
0 27
 
1.8%
1 606
41.2%
2 159
 
10.8%
3 464
31.6%
4 82
 
5.6%
5 132
 
9.0%
ValueCountFrequency (%)
5 132
 
9.0%
4 82
 
5.6%
3 464
31.6%
2 159
 
10.8%
1 606
41.2%
0 27
 
1.8%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
453 
4
446 
2
287 
1
284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Length

2023-05-12T15:24:56.475340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:56.519820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring characters

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
882 
0
588 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 882
60.0%
0 588
40.0%

Length

2023-05-12T15:24:56.558752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:56.601147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 882
60.0%
0 588
40.0%

Most occurring characters

ValueCountFrequency (%)
1 882
60.0%
0 588
40.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 882
60.0%
0 588
40.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 882
60.0%
0 588
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 882
60.0%
0 588
40.0%

Hourly_Rate
Real number (ℝ)

Distinct71
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.891156
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:56.642895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383.75
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation20.329428
Coefficient of variation (CV)0.30853044
Kurtosis-1.1963985
Mean65.891156
Median Absolute Deviation (MAD)18
Skewness-0.032310953
Sum96860
Variance413.28563
MonotonicityNot monotonic
2023-05-12T15:24:56.693422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 29
 
2.0%
98 28
 
1.9%
42 28
 
1.9%
48 28
 
1.9%
84 28
 
1.9%
57 27
 
1.8%
79 27
 
1.8%
96 27
 
1.8%
54 26
 
1.8%
52 26
 
1.8%
Other values (61) 1196
81.4%
ValueCountFrequency (%)
30 19
1.3%
31 15
1.0%
32 24
1.6%
33 19
1.3%
34 12
0.8%
35 18
1.2%
36 18
1.2%
37 18
1.2%
38 13
0.9%
39 17
1.2%
ValueCountFrequency (%)
100 19
1.3%
99 20
1.4%
98 28
1.9%
97 21
1.4%
96 27
1.8%
95 23
1.6%
94 22
1.5%
93 16
1.1%
92 25
1.7%
91 18
1.2%

Job_Involvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Length

2023-05-12T15:24:56.741876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:56.786329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Job_Role
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4585034
Minimum0
Maximum8
Zeros131
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:56.824719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4618213
Coefficient of variation (CV)0.55216315
Kurtosis-1.1927348
Mean4.4585034
Median Absolute Deviation (MAD)2
Skewness-0.35726992
Sum6554
Variance6.0605641
MonotonicityNot monotonic
2023-05-12T15:24:56.864074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 326
22.2%
6 292
19.9%
2 259
17.6%
4 145
9.9%
0 131
8.9%
3 102
 
6.9%
8 83
 
5.6%
5 80
 
5.4%
1 52
 
3.5%
ValueCountFrequency (%)
0 131
8.9%
1 52
 
3.5%
2 259
17.6%
3 102
 
6.9%
4 145
9.9%
5 80
 
5.4%
6 292
19.9%
7 326
22.2%
8 83
 
5.6%
ValueCountFrequency (%)
8 83
 
5.6%
7 326
22.2%
6 292
19.9%
5 80
 
5.4%
4 145
9.9%
3 102
 
6.9%
2 259
17.6%
1 52
 
3.5%
0 131
8.9%

Job_Satisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Length

2023-05-12T15:24:56.913799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:56.958513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring characters

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Marital_Status
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
673 
2
470 
0
327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 673
45.8%
2 470
32.0%
0 327
22.2%

Length

2023-05-12T15:24:56.998246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:57.040569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 673
45.8%
2 470
32.0%
0 327
22.2%

Most occurring characters

ValueCountFrequency (%)
1 673
45.8%
2 470
32.0%
0 327
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 673
45.8%
2 470
32.0%
0 327
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 673
45.8%
2 470
32.0%
0 327
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 673
45.8%
2 470
32.0%
0 327
22.2%

Monthly_Income
Real number (ℝ)

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.9313
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:57.084560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.9568
Coefficient of variation (CV)0.72397455
Kurtosis1.0052327
Mean6502.9313
Median Absolute Deviation (MAD)2199
Skewness1.3698167
Sum9559309
Variance22164857
MonotonicityNot monotonic
2023-05-12T15:24:57.135859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2342 4
 
0.3%
6142 3
 
0.2%
2741 3
 
0.2%
2559 3
 
0.2%
2610 3
 
0.2%
2451 3
 
0.2%
5562 3
 
0.2%
3452 3
 
0.2%
2380 3
 
0.2%
6347 3
 
0.2%
Other values (1339) 1439
97.9%
ValueCountFrequency (%)
1009 1
0.1%
1051 1
0.1%
1052 1
0.1%
1081 1
0.1%
1091 1
0.1%
1102 1
0.1%
1118 1
0.1%
1129 1
0.1%
1200 1
0.1%
1223 1
0.1%
ValueCountFrequency (%)
19999 1
0.1%
19973 1
0.1%
19943 1
0.1%
19926 1
0.1%
19859 1
0.1%
19847 1
0.1%
19845 1
0.1%
19833 1
0.1%
19740 1
0.1%
19717 1
0.1%

Monthly_Rate
Real number (ℝ)

Distinct1427
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.103
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:57.191842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3384.55
Q18047
median14235.5
Q320461.5
95-th percentile25431.9
Maximum26999
Range24905
Interquartile range (IQR)12414.5

Descriptive statistics

Standard deviation7117.786
Coefficient of variation (CV)0.4972916
Kurtosis-1.2149561
Mean14313.103
Median Absolute Deviation (MAD)6206.5
Skewness0.018577808
Sum21040262
Variance50662878
MonotonicityNot monotonic
2023-05-12T15:24:57.240393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4223 3
 
0.2%
9150 3
 
0.2%
9558 2
 
0.1%
12858 2
 
0.1%
22074 2
 
0.1%
25326 2
 
0.1%
9096 2
 
0.1%
13008 2
 
0.1%
12355 2
 
0.1%
7744 2
 
0.1%
Other values (1417) 1448
98.5%
ValueCountFrequency (%)
2094 1
0.1%
2097 1
0.1%
2104 1
0.1%
2112 1
0.1%
2122 1
0.1%
2125 2
0.1%
2137 1
0.1%
2227 1
0.1%
2243 1
0.1%
2253 1
0.1%
ValueCountFrequency (%)
26999 1
0.1%
26997 1
0.1%
26968 1
0.1%
26959 1
0.1%
26956 1
0.1%
26933 1
0.1%
26914 1
0.1%
26897 1
0.1%
26894 1
0.1%
26862 1
0.1%

Num_comp_worked
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6931973
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:57.282105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009
Coefficient of variation (CV)0.92752545
Kurtosis0.010213817
Mean2.6931973
Median Absolute Deviation (MAD)1
Skewness1.0264711
Sum3959
Variance6.240049
MonotonicityNot monotonic
2023-05-12T15:24:57.315359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 521
35.4%
0 197
 
13.4%
3 159
 
10.8%
2 146
 
9.9%
4 139
 
9.5%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
9 52
 
3.5%
8 49
 
3.3%
ValueCountFrequency (%)
0 197
 
13.4%
1 521
35.4%
2 146
 
9.9%
3 159
 
10.8%
4 139
 
9.5%
5 63
 
4.3%
6 70
 
4.8%
7 74
 
5.0%
8 49
 
3.3%
9 52
 
3.5%
ValueCountFrequency (%)
9 52
 
3.5%
8 49
 
3.3%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
4 139
 
9.5%
3 159
 
10.8%
2 146
 
9.9%
1 521
35.4%
0 197
 
13.4%

Work_Overtime
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1054 
1
416 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1054
71.7%
1 416
 
28.3%

Length

2023-05-12T15:24:57.353722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:57.395300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1054
71.7%
1 416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
0 1054
71.7%
1 416
 
28.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1054
71.7%
1 416
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1054
71.7%
1 416
 
28.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1054
71.7%
1 416
 
28.3%

Salary_Hike_Percentage
Real number (ℝ)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209524
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:57.427520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6599377
Coefficient of variation (CV)0.2406346
Kurtosis-0.30059822
Mean15.209524
Median Absolute Deviation (MAD)2
Skewness0.82112798
Sum22358
Variance13.395144
MonotonicityNot monotonic
2023-05-12T15:24:57.461952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 210
14.3%
13 209
14.2%
14 201
13.7%
12 198
13.5%
15 101
6.9%
18 89
6.1%
17 82
 
5.6%
16 78
 
5.3%
19 76
 
5.2%
22 56
 
3.8%
Other values (5) 170
11.6%
ValueCountFrequency (%)
11 210
14.3%
12 198
13.5%
13 209
14.2%
14 201
13.7%
15 101
6.9%
16 78
 
5.3%
17 82
 
5.6%
18 89
6.1%
19 76
 
5.2%
20 55
 
3.7%
ValueCountFrequency (%)
25 18
 
1.2%
24 21
 
1.4%
23 28
 
1.9%
22 56
3.8%
21 48
3.3%
20 55
3.7%
19 76
5.2%
18 89
6.1%
17 82
5.6%
16 78
5.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
1244 
4
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Length

2023-05-12T15:24:57.503064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:57.544856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring characters

ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
459 
4
432 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Length

2023-05-12T15:24:57.580085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:57.623935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring characters

ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Length

2023-05-12T15:24:57.663259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:57.708282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Total_working_years
Real number (ℝ)

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279592
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:57.752248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7807817
Coefficient of variation (CV)0.68981057
Kurtosis0.91826954
Mean11.279592
Median Absolute Deviation (MAD)4
Skewness1.1171719
Sum16581
Variance60.540563
MonotonicityNot monotonic
2023-05-12T15:24:57.969459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 202
 
13.7%
6 125
 
8.5%
8 103
 
7.0%
9 96
 
6.5%
5 88
 
6.0%
7 81
 
5.5%
1 81
 
5.5%
4 63
 
4.3%
12 48
 
3.3%
3 42
 
2.9%
Other values (30) 541
36.8%
ValueCountFrequency (%)
0 11
 
0.7%
1 81
5.5%
2 31
 
2.1%
3 42
 
2.9%
4 63
4.3%
5 88
6.0%
6 125
8.5%
7 81
5.5%
8 103
7.0%
9 96
6.5%
ValueCountFrequency (%)
40 2
 
0.1%
38 1
 
0.1%
37 4
0.3%
36 6
0.4%
35 3
 
0.2%
34 5
0.3%
33 7
0.5%
32 9
0.6%
31 9
0.6%
30 7
0.5%

Train_time_last_year
Real number (ℝ)

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:58.011363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892706
Coefficient of variation (CV)0.46056569
Kurtosis0.49499299
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55312417
Sum4115
Variance1.6622187
MonotonicityNot monotonic
2023-05-12T15:24:58.044389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
1 71
 
4.8%
6 65
 
4.4%
0 54
 
3.7%
ValueCountFrequency (%)
0 54
 
3.7%
1 71
 
4.8%
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
6 65
 
4.4%
ValueCountFrequency (%)
6 65
 
4.4%
5 119
 
8.1%
4 123
 
8.4%
3 491
33.4%
2 547
37.2%
1 71
 
4.8%
0 54
 
3.7%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
893 
2
344 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Length

2023-05-12T15:24:58.083447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T15:24:58.127980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1470
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Years_company
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081633
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:58.170186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1265252
Coefficient of variation (CV)0.87419841
Kurtosis3.9355088
Mean7.0081633
Median Absolute Deviation (MAD)3
Skewness1.7645295
Sum10302
Variance37.53431
MonotonicityNot monotonic
2023-05-12T15:24:58.217570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 196
13.3%
1 171
11.6%
3 128
8.7%
2 127
8.6%
10 120
8.2%
4 110
 
7.5%
7 90
 
6.1%
9 82
 
5.6%
8 80
 
5.4%
6 76
 
5.2%
Other values (27) 290
19.7%
ValueCountFrequency (%)
0 44
 
3.0%
1 171
11.6%
2 127
8.6%
3 128
8.7%
4 110
7.5%
5 196
13.3%
6 76
 
5.2%
7 90
6.1%
8 80
5.4%
9 82
5.6%
ValueCountFrequency (%)
40 1
 
0.1%
37 1
 
0.1%
36 2
 
0.1%
34 1
 
0.1%
33 5
0.3%
32 3
0.2%
31 3
0.2%
30 1
 
0.1%
29 2
 
0.1%
27 2
 
0.1%

Years_current_role
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2292517
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:58.262740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137
Coefficient of variation (CV)0.85668513
Kurtosis0.47742077
Mean4.2292517
Median Absolute Deviation (MAD)3
Skewness0.91736316
Sum6217
Variance13.127122
MonotonicityNot monotonic
2023-05-12T15:24:58.302028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 372
25.3%
0 244
16.6%
7 222
15.1%
3 135
 
9.2%
4 104
 
7.1%
8 89
 
6.1%
9 67
 
4.6%
1 57
 
3.9%
6 37
 
2.5%
5 36
 
2.4%
Other values (9) 107
 
7.3%
ValueCountFrequency (%)
0 244
16.6%
1 57
 
3.9%
2 372
25.3%
3 135
 
9.2%
4 104
 
7.1%
5 36
 
2.4%
6 37
 
2.5%
7 222
15.1%
8 89
 
6.1%
9 67
 
4.6%
ValueCountFrequency (%)
18 2
 
0.1%
17 4
 
0.3%
16 7
 
0.5%
15 8
 
0.5%
14 11
 
0.7%
13 14
 
1.0%
12 10
 
0.7%
11 22
 
1.5%
10 29
2.0%
9 67
4.6%

Years_Promotion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1877551
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:58.344710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2224303
Coefficient of variation (CV)1.4729392
Kurtosis3.6126731
Mean2.1877551
Median Absolute Deviation (MAD)1
Skewness1.98429
Sum3216
Variance10.384057
MonotonicityNot monotonic
2023-05-12T15:24:58.381683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
7 76
 
5.2%
4 61
 
4.1%
3 52
 
3.5%
5 45
 
3.1%
6 32
 
2.2%
11 24
 
1.6%
8 18
 
1.2%
Other values (6) 65
 
4.4%
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
3 52
 
3.5%
4 61
 
4.1%
5 45
 
3.1%
6 32
 
2.2%
7 76
 
5.2%
8 18
 
1.2%
9 17
 
1.2%
ValueCountFrequency (%)
15 13
 
0.9%
14 9
 
0.6%
13 10
 
0.7%
12 10
 
0.7%
11 24
 
1.6%
10 6
 
0.4%
9 17
 
1.2%
8 18
 
1.2%
7 76
5.2%
6 32
2.2%

Years_curr_mangaer
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1231293
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-05-12T15:24:58.423455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5681361
Coefficient of variation (CV)0.86539517
Kurtosis0.17105808
Mean4.1231293
Median Absolute Deviation (MAD)3
Skewness0.83345099
Sum6061
Variance12.731595
MonotonicityNot monotonic
2023-05-12T15:24:58.462606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 344
23.4%
0 263
17.9%
7 216
14.7%
3 142
9.7%
8 107
 
7.3%
4 98
 
6.7%
1 76
 
5.2%
9 64
 
4.4%
5 31
 
2.1%
6 29
 
2.0%
Other values (8) 100
 
6.8%
ValueCountFrequency (%)
0 263
17.9%
1 76
 
5.2%
2 344
23.4%
3 142
9.7%
4 98
 
6.7%
5 31
 
2.1%
6 29
 
2.0%
7 216
14.7%
8 107
 
7.3%
9 64
 
4.4%
ValueCountFrequency (%)
17 7
 
0.5%
16 2
 
0.1%
15 5
 
0.3%
14 5
 
0.3%
13 14
 
1.0%
12 18
 
1.2%
11 22
 
1.5%
10 27
 
1.8%
9 64
4.4%
8 107
7.3%

Interactions

2023-05-12T15:24:54.701189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:42.991965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.791524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.546229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.421540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.142286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.900599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.658247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.420761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.132309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.006457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.751934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.521259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.261075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.027010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.786654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.744753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.041962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.833663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.588049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.463141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.186145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.942922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.702254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.461276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.171670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.047606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.795468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.563253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.303643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.069092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.828421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.792107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.125511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.881587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.634916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.508532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.233633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.989828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.749688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.504419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.217754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.094515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.843106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.611104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.351181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.116590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.874661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.839763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.174614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.930694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.681220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.552271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.282567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.037387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.796063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.548805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.261061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.139915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.890879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.656089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.398360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.164189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.920947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.884623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.231664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.976195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.725303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.595136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.327430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.083402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.842153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.590837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.303488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.185391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.937351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.700593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.444625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.210476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.965975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.933330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.286950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.023367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.773181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.640516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.374213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.131802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.889993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.637230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.349060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.234096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.987237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.747258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.498015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.259174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.013871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.982698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.336203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.073972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.821824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.687417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.422523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.181099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.939502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.683338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.395205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.281200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.036508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.795980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.546688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.310054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.063257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:55.032046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.382268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.121876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.869481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.733581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.472045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.230569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.987079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.729117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.441399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.330584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.087108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.843665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.596856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.359176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.111838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:55.076884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.424459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.166385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.912576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.776959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.514923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.273571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.033263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.768993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.483432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.373433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.131525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.887074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.641361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.402944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.318621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:55.120324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.464080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.208988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.956574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.817075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.558950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.317183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.076478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.810211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.522561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.417308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.176320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.929480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.685783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.446209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.361350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:55.169984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.508471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.255225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.002068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.862673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.607049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.363910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.124610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.854360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.566976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.463260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.226425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.975650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.732493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.494793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.407855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:55.220356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.555448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.306675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.050867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.910698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.657017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.415311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.175733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.902130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.613272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.512370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.275285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.023371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.782013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.545049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.458474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:55.267187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.599818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.352590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.097280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.954857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.703084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.461569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.222697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.945928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.657636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.557427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.325376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.069476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.827674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.592436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.505359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:55.318766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.646352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.401710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.146387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.001963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.753308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.511336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.273746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.993832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.870843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.607099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.373638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.117283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.877812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.641616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.555032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:55.367856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.694398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.450162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.194142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.050699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.803459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.561243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.322748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.039833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.916534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.655809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.423171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.166416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.927684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.689836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.604668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:55.416563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:43.744106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:44.497079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:45.374083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.095734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:46.851631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:47.609129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:48.371885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.085214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:49.959538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:50.702942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:51.471134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.212297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:52.977399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:53.737689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T15:24:54.650966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-12T15:24:58.520944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
AgeDailyRateDistance_From_HomeEducation_FieldHourly_RateJob_RoleMonthly_IncomeMonthly_RateNum_comp_workedSalary_Hike_PercentageTotal_working_yearsTrain_time_last_yearYears_companyYears_current_roleYears_PromotionYears_curr_mangaerAttritionBusiness_TravelDepartmentEducation_in_yearsEnvironment_SatisfactionGenderJob_InvolvementJob_SatisfactionMarital_StatusWork_OvertimePerformance_RatingRelationship_SatisfactionStack_Option_Levelwork_life_balance
Age1.0000.007-0.019-0.0450.029-0.1280.4720.0170.3530.0080.6570.0000.2520.1980.1740.1950.2130.0410.0000.1530.0060.0000.0250.0000.1410.0000.0000.0350.0930.033
DailyRate0.0071.000-0.0030.0340.024-0.0060.016-0.0320.0370.0250.021-0.011-0.0100.007-0.038-0.0050.0620.0290.0000.0170.0000.0310.0160.0000.0850.0000.0000.0000.0400.012
Distance_From_Home-0.019-0.0031.0000.0170.0200.0160.0030.040-0.0100.030-0.003-0.0250.0110.014-0.0050.0040.0670.0230.0000.0000.0000.0300.0280.0000.0000.0660.0580.0250.0150.000
Education_Field-0.0450.0340.0171.000-0.0260.019-0.035-0.028-0.012-0.002-0.0220.049-0.001-0.0070.0130.0080.0870.0000.5880.0550.0310.0000.0000.0170.0000.0000.0000.0400.0320.027
Hourly_Rate0.0290.0240.020-0.0261.000-0.020-0.020-0.0150.019-0.010-0.0120.000-0.029-0.034-0.052-0.0140.0440.0000.0000.0000.0000.0000.0000.0100.0000.0640.0000.0000.0520.000
Job_Role-0.128-0.0060.0160.019-0.0201.000-0.0440.006-0.0660.002-0.1480.022-0.055-0.013-0.019-0.0350.2310.0000.9370.0510.0000.0740.0000.0000.0610.0000.0000.0300.0390.029
Monthly_Income0.4720.0160.003-0.035-0.020-0.0441.0000.0540.190-0.0340.710-0.0350.4640.3950.2650.3650.2170.0250.1870.0940.0000.0460.0460.0000.0610.0000.0000.0430.0560.000
Monthly_Rate0.017-0.0320.040-0.028-0.0150.0060.0541.0000.020-0.0050.013-0.010-0.030-0.007-0.016-0.0350.0100.0000.0000.0370.0000.0000.0000.0480.0000.0000.0150.0550.0000.034
Num_comp_worked0.3530.037-0.010-0.0120.019-0.0660.1900.0201.0000.0000.315-0.047-0.171-0.128-0.067-0.1440.1070.0000.0320.1010.0000.0000.0000.0000.0380.0000.0000.0000.0000.051
Salary_Hike_Percentage0.0080.0250.030-0.002-0.0100.002-0.034-0.0050.0001.000-0.026-0.004-0.054-0.026-0.055-0.0260.0000.0300.0000.0210.0000.0490.0360.0000.0000.0000.9970.0270.0000.000
Total_working_years0.6570.021-0.003-0.022-0.012-0.1480.7100.0130.315-0.0261.000-0.0140.5940.4930.3350.4950.2080.0000.0240.0950.0000.0000.0000.0240.0690.0000.0000.0310.0640.000
Train_time_last_year0.000-0.011-0.0250.0490.0000.022-0.035-0.010-0.047-0.004-0.0141.0000.0010.0050.010-0.0120.0790.0000.0000.0270.0000.0000.0130.0210.0000.0990.0000.0000.0000.000
Years_company0.252-0.0100.011-0.001-0.029-0.0550.464-0.030-0.171-0.0540.5940.0011.0000.8540.5200.8430.1730.0000.0000.0710.0310.0660.0530.0000.0000.0180.0000.0000.0120.020
Years_current_role0.1980.0070.014-0.007-0.034-0.0130.395-0.007-0.128-0.0260.4930.0050.8541.0000.5060.7250.1690.0000.0000.0290.0360.0790.0000.0000.0400.0420.0310.0000.0230.025
Years_Promotion0.174-0.038-0.0050.013-0.052-0.0190.265-0.016-0.067-0.0550.3350.0100.5200.5061.0000.4670.0270.0300.0000.0000.0000.0000.0000.0000.0350.0110.0000.0500.0560.000
Years_curr_mangaer0.195-0.0050.0040.008-0.014-0.0350.365-0.035-0.144-0.0260.495-0.0120.8430.7250.4671.0000.1790.0640.0000.0000.0000.0000.0440.0000.0000.0000.0300.0000.0300.031
Attrition0.2130.0620.0670.0870.0440.2310.2170.0100.1070.0000.2080.0790.1730.1690.0270.1791.0000.1230.0770.0000.1150.0090.1320.0990.1730.2430.0000.0390.1980.095
Business_Travel0.0410.0290.0230.0000.0000.0000.0250.0000.0000.0300.0000.0000.0000.0000.0300.0640.1231.0000.0000.0000.0000.0370.0160.0000.0350.0240.0000.0000.0000.000
Department0.0000.0000.0000.5880.0000.9370.1870.0000.0320.0000.0240.0000.0000.0000.0000.0000.0770.0001.0000.0000.0180.0260.0000.0290.0300.0000.0000.0200.0000.047
Education_in_years0.1530.0170.0000.0550.0000.0510.0940.0370.1010.0210.0950.0270.0710.0290.0000.0000.0000.0000.0001.0000.0190.0000.0000.0150.0000.0010.0000.0160.0270.000
Environment_Satisfaction0.0060.0000.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.0310.0360.0000.0000.1150.0000.0180.0191.0000.0000.0340.0000.0190.0600.0000.0000.0000.000
Gender0.0000.0310.0300.0000.0000.0740.0460.0000.0000.0490.0000.0000.0660.0790.0000.0000.0090.0370.0260.0000.0001.0000.0000.0000.0320.0310.0000.0000.0000.000
Job_Involvement0.0250.0160.0280.0000.0000.0000.0460.0000.0000.0360.0000.0130.0530.0000.0000.0440.1320.0160.0000.0000.0340.0001.0000.0000.0240.0000.0000.0000.0220.000
Job_Satisfaction0.0000.0000.0000.0170.0100.0000.0000.0480.0000.0000.0240.0210.0000.0000.0000.0000.0990.0000.0290.0150.0000.0000.0001.0000.0000.0220.0260.0000.0000.000
Marital_Status0.1410.0850.0000.0000.0000.0610.0610.0000.0380.0000.0690.0000.0000.0400.0350.0000.1730.0350.0300.0000.0190.0320.0240.0001.0000.0000.0000.0250.5810.000
Work_Overtime0.0000.0000.0660.0000.0640.0000.0000.0000.0000.0000.0000.0990.0180.0420.0110.0000.2430.0240.0000.0010.0600.0310.0000.0220.0001.0000.0000.0250.0000.000
Performance_Rating0.0000.0000.0580.0000.0000.0000.0000.0150.0000.9970.0000.0000.0000.0310.0000.0300.0000.0000.0000.0000.0000.0000.0000.0260.0000.0001.0000.0000.0000.000
Relationship_Satisfaction0.0350.0000.0250.0400.0000.0300.0430.0550.0000.0270.0310.0000.0000.0000.0500.0000.0390.0000.0200.0160.0000.0000.0000.0000.0250.0250.0001.0000.0300.000
Stack_Option_Level0.0930.0400.0150.0320.0520.0390.0560.0000.0000.0000.0640.0000.0120.0230.0560.0300.1980.0000.0000.0270.0000.0000.0220.0000.5810.0000.0000.0301.0000.019
work_life_balance0.0330.0120.0000.0270.0000.0290.0000.0340.0510.0000.0000.0000.0200.0250.0000.0310.0950.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0000.0191.000

Missing values

2023-05-12T15:24:55.508521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-12T15:24:55.683612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeAttritionBusiness_TravelDailyRateDepartmentDistance_From_HomeEducation_in_yearsEducation_FieldEnvironment_SatisfactionGenderHourly_RateJob_InvolvementJob_RoleJob_SatisfactionMarital_StatusMonthly_IncomeMonthly_RateNum_comp_workedWork_OvertimeSalary_Hike_PercentagePerformance_RatingRelationship_SatisfactionStack_Option_LevelTotal_working_yearsTrain_time_last_yearwork_life_balanceYears_companyYears_current_roleYears_PromotionYears_curr_mangaer
0411211022121209437425993.019479.08111.03108.0016.04.00.05.0
149012791811316126215130.024907.01023.044110.03310.07.01.07.0
2371213731224419222322090.02396.06115.03207.0330.00.00.00.0
3330113921341405636312909.023159.01111.03308.0338.07.03.00.0
427025911213114032213468.016632.09012.03416.0332.02.02.02.0
5320110051221417932423068.011864.00013.03308.0227.07.03.06.0
6590213241333308142112670.09964.04120.041312.0321.00.00.00.0
73002135812411416732302693.013335.01022.04211.0231.00.00.00.0
8380121612331414424329526.08787.00021.042010.0239.07.01.08.0
93602129912733319430315237.016577.06013.032217.0327.07.07.07.0
AgeAttritionBusiness_TravelDailyRateDepartmentDistance_From_HomeEducation_in_yearsEducation_FieldEnvironment_SatisfactionGenderHourly_RateJob_InvolvementJob_RoleJob_SatisfactionMarital_StatusMonthly_IncomeMonthly_RateNum_comp_workedWork_OvertimeSalary_Hike_PercentagePerformance_RatingRelationship_SatisfactionStack_Option_LevelTotal_working_yearsTrain_time_last_yearwork_life_balanceYears_companyYears_current_roleYears_PromotionYears_curr_mangaer
1460290246812843407326123785.08489.01014.03205.0315.04.00.04.0
14615012410228324139271010854.016586.04113.032120.0333.02.02.00.0
14623902722224122060274112031.08828.00011.031121.02220.09.09.06.0
146331003251533217434129936.03787.00019.032010.0239.04.01.07.0
1464260211672534403028322966.021378.00018.03405.0234.02.00.00.0
1465360188412323314142412571.012290.04017.033117.0335.02.00.03.0
146639026131613414220119991.021457.04015.03119.0537.07.01.07.0
146727021551431218744216142.05174.01120.04216.0036.02.00.03.0
1468490110232233416327215390.013243.02014.034017.0329.06.00.08.0
146934026281833218242314404.010228.02012.03106.0344.03.01.02.0